Bibliographic record
Abstract
Purpose This paper aims to investigate the extent of corruption globally, explains its social and economic consequences and introduces a model, composed of corporate governance mechanisms, internal controls and red flag analyses, which organizations can apply to prevent corruption. Design/methodology/approach This study uses criminology theories to analyze corruption and its prevention. Findings The global cost of bribery alone is estimated at US$1tn annually, not including costs resulting from non-completion and deficient completion of development projects (World Bank Institute, 2004). This paper shows that an effective prevention model should include a positive work environment and ethical governance; the implementation of a compliance risk management program with fraud risk assessments; an accessible psychological assistance program for employees; regular employee anti-fraud training; the implementation of targeted internal controls such as proper segregation of organizational duties; the adoption of fair compensation levels and realistic individual performance goals; a user-friendly and anonymous reporting mechanism; and independent and regular analyses of abnormal patterns (red flags). Research limitations/implications This paper extends previous research by tying together disparate factors into a cohesive model for the prevention of corruption. Practical implications The prevention model developed in this paper assists in deterring corruption, improving internal controls, improving the likelihood of detection and reducing opportunities to perpetrate corruption. By reducing the risk of corruption, this model also helps organizations and governments reduce project costs (public spending) and improve project quality, thus promoting economic competitiveness. Originality/value A comprehensive prevention model is developed to help curtail corruption and its devastating effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".